Optimized fuzzy rule-based energy management for a battery-less PV/wind-BWRO desalination system

Abstract

Coupling water desalination processes with Renewable Energy Sources (RESs) can be a sustainable and ecological approach to the global water/energy supply crisis. In this regard, small scale standalone battery less Brackish Water Reverse Osmosis (BWRO) desalination system powered by a hybrid PV/Wind RES is expected to meet freshwater demand of a small isolated community. One particularity of the proposed architecture deals with the absence of electrochemical storage; only taking benefit of hydraulic storage in water tanks when RE is available. This study puts forward the prime importance of Water/ Power flows management optimization. For this purpose, an online Fuzzy Logic based Energy Manage ment Strategy (FLEMS) is proposed. Firstly, a Hand Made Fuzzy Inference System (HMFIS) was designed to identify the instantaneous power sharing between the system hydro mechanical processes (well pumping, water storage and desalination processes). Secondly, an offline genetic algorithm optimization was applied on the HMFIS design in order to optimize the power sharing factor and maximize freshwater production. Then, when applying unknown power profile, the optimized FLEMS demonstrated its per formance to improve the system energy efficiency and enhance the brackish water (from 16.7% in autumn to 63% in summer) and freshwater production (to 3.3% during autumn for instance) compared with the HMFIS based EMS.

Systemic design and energy management of a standalone battery-less PV/Wind driven brackish water reverse osmosis desalination system

“…As a result, freshwater productivity can be improved. This point has been highlighted in previous work [51]. Moreover, besides the advantage of being environmentally friendly, another advantage of using hydraulic storage over electrochemical storage device is the simplicity of such a configuration (no need to control laws), as well as it offers lower cost alternative (no need to additional power converters for control and no maintenance requirements).…”

“…For example, a genetic algorithm optimized fuzzy logic-based energy management strategy was previously developed by authors (ref. [51]) and applied to the first pump combination Comb.1 of the desalination unit with a storage tank capacity of 2.18 m 3 . This helps to reduce the number of successive On/Off of motor-pumps.…”

“…In order to validate the developed models of the two hydro-mechanical processes, model-based simulation results are compared to the models extracted from experimental characterizations (in steady-state) of each hydromechanical process of the desalination experimental set-up (for more details on the experimental characterizations see sub-section 3.2) in ref. [51]). This comparison is illustrated by Fig.…”

“…In this work, the suitable power range [Pmin -Pmax] of each pumping device has been experimentally identified (for more details see ref. [51]) while functioning with a given hydraulic load.…”

Joint optimisation of sizing and fuzzy logic power management of a hybrid storage system considering economic reliability indices

“…• The BT self-discharge is neglected and the BT has no memory impact. [20] size location linear programming BT storage system + PV reduce the time-variable price profile and storage cost power loss reduction [21] size intelligent flower pollination algorithm PV + wind turbine + fuel cell minimise the total net present cost smooth output generation [22] FLEMS GA optimisation PV panels + batteries + wind turbine optimise the power sharing factor maximise freshwater production enhance the brackish water [23] This study proposes a BT model characterised by high accuracy (SOC estimation, definition of charge and discharge limits) and parameters available in the manufacturer's datasheet. The principal of this modelling approach is to calculate in a precise way and in a very short time the BT voltage according to the discharge capacity by using a Lookup table method [27].…”